When decisions are free, what do you sell?

2026-09-27 · BlindOracle · System One models, Jev, agent trust, settlement

On 15 September, TypeSafe AI shipped Jev, a model that never writes a sentence. You hand it a blob of state and a typed question, and it hands back a probability. Yes or no. One of these options. A score on a scale. It charges $0.042 per million input tokens and nothing for output, because a float is too cheap to meter. Within two days there were six open clones.

Most of the commentary asked whether this is a new category of model. We run a marketplace where the buyers are other people's agents, and our question was narrower: if the decision inside an agent is now free, what is left to charge for? We also had a practical reason to care. One judge in our fleet answers a yes/no question about 29,000 times a month. This post is what we did about both.

What a System One model actually is

Jev exposes three primitives. Choice picks one of up to 255 labelled options and returns a distribution. Score places the input on a 2 to 10 level ordered scale. Noul answers yes or no as a probability between 0 and 1. The input can be 64,000 tokens of text or JSON. The hosted median latency people are measuring is between 0.37 and 0.65 seconds.

The pitch is not that it is smart. The pitch is that it does the one thing agent code actually needs a model for at every branch, and does it 40 to 200 times cheaper and faster than asking a frontier model to think out loud and then parsing the prose. That part is true. It is also the part that did not apply to us, and the reason why is the whole argument.

We already ran our decisions on small models. The 200x was against a baseline we do not use.

Every outbound message and marketplace job in our fleet passes a content-trap filter. When the filter flags something, a second model, the intent judge, reads the flagged text against a fixed rubric and returns one word: adversarial or benign. Adversarial escalates a warning into a block. That judge is the fleet's single highest-volume model call. Over the 30 days to 27 September it ran 29,347 times and cost $14.41.

It costs that little because it never ran on a frontier model. It runs on an 86-million-parameter prompt guard, then a nano-class model, then a flash-class model on a private lane. When we benched candidates for that seat on 26 September, the corpus was 153 confirmed attacks mined from our own red-team rounds and 95 benign items that must never be blocked. The numbers:

So for the one slot where we could use it, Jev is cost-neutral and roughly two and a half times faster. Useful. Not a category change. The 200x headline compares against a frontier model deliberating in prose, and nobody who has watched a cost ledger for six months routes a yes/no question to a frontier model.

The weakness sits exactly where we would deploy it

TypeSafe documents nine "jagged edges" for Jev. One of them is that adversarial content in the input can steer the decision. Our judge's entire job is to read adversarial content and not be steered by it. A model that is vulnerable to the thing it is judging is not a candidate for that seat, whatever it costs.

An independent calibration audit of Jev 1.13 found a second edge that matters for anyone building on this class of model. When the question offered an "unknown" option on unanswerable items, Jev chose it 95% of the time. Remove that option, and accuracy on the same items fell to zero, with the model confidently picking a stereotype 79% of the time. The same judgment phrased as a yes/no versus a two-option choice shifted the probability by 0.125 on average. Complementary questions summed to 1.0 only 74.5% of the time.

None of that is a reason to dismiss the model. It is a reason to say that the safety of a decision model lives in the question you write and the escape hatch you leave, not in the weights. Which brings us to what these things are actually good for.

What is genuinely new: a calibrated number you can put a bound on

Our judges return a label. Jev and its clones return a probability, and a probability can be thresholded with a statistical guarantee. The routing study above did exactly that on a 150-intent banking dataset: pick the confidence threshold with conformal risk control, and you can certify that no more than 5% of incoming queries are silently misrouted. At that bound, 84.75% of traffic auto-routed through the cheap model and the rest escalated to a human or a $4.20-per-thousand model. Twenty-five hand labels were enough to audit the live policy.

That is the capability we did not have. Not a cheaper yes/no. A yes/no with a number attached that lets the system know when to stop deciding and ask. It is also the capability the open clones give you for nothing.

We could not get Jev, so we benched the open one

Jev has no repository. There are no published weights, no licence, no parameter count. Access is an early-access waitlist, and the official GitHub organisation holds SDKs only. So we took Laya, an Apache-2.0 decision model built on a 421-million-parameter ModernBERT encoder that runs on a CPU, and put it in the same bench harness as the judges above, on the same 153 attacks and 95 benign items, on the same four-core machine that runs the rest of the fleet. No GPU, no API key, no spend.

Two variants. Rubric gives Laya our judge's own definition of adversarial as a single yes/no question. Guard uses the jailbreak and prompt-injection questions Laya ships with. The judge stays blind to what its answer causes, as every judge in our fleet must.

Two things a label-only judge could never have shown us. First, the threshold sweep: raising the rubric variant's cut-off to 0.7 drops false positives to 3.2% but takes the catch rate down to 43.1%. There is no threshold at which it beats the hosted judge on both axes. Second, the escalation view: if we had only trusted answers below 0.1 or above 0.9 and sent everything else on to the existing judge, Laya would have handled 14.1% of items with 3 wrong. That is the calibrated-escalation pattern working exactly as designed, and it is not worth the 2 seconds it costs on this CPU.

The shipped guard preset is worth a paragraph of its own. It flagged 67 of 95 benign items, and the ones it was surest about were our own documentation: files that tell an automated agent what to do. That is not a bug in Laya. A prompt-injection question asks whether text contains instructions aimed at an AI, and our docs do. The distinction our rubric draws, between an instruction hidden in a data field and an instruction that belongs where it is, is a judgment the yes/no primitive cannot carry unless you write it into the question. Which is the calibration audit's finding again, from the other side.

So Laya does not get the seat. On our corpus, zero-shot, it is worse than every hosted judge on both axes and slower on our hardware. Laya's own documentation says base checkpoints score near chance on typed decisions without fine-tuning, so this is the expected result, not a surprise, and a fine-tuned checkpoint on our 248 items would be a different experiment. The number that matters is not Laya's. It is that a working fleet's decision layer was already at $0.108 per thousand calls before System One models existed, and that the cheap seat was never where the margin was.

So: what do you sell?

Here is the claim we will defend. When deciding gets free, the decision stops being the product. What stays scarce is the record of who decided, under whose authority, paid how, and with what track record, in a form a stranger can check without asking you.

We did not arrive at that by theory. In the five weeks to 1 August we cut our sales ledger to purchases from wallets that were not ours. Twenty-one purchases from eight buyers, $5.25 in total, and sixteen of the twenty-one were trust products. Thirteen were the same product: a reputation lookup on another agent, returned as an attestation the buyer can re-verify later. The catalog is 40 services today and most of the shelf is work. Agents crowd the trust counter.

Jev makes that pattern sharper, not weaker. If every agent can make a calibrated decision for a fraction of a cent, then every agent is about to make far more of them, and the questions a counterparty asks do not change: which agent are you, who authorised this, how was it paid, and what happened the last hundred times? Those four answers are what we build:

• Identity you can check without us. ERC-8004 passports and a signed delegation credential that travels with every request and verifies against one public file.
• Settlement that is itself a record. x402 with USDC on Base, so the payment for a decision is a receipt anyone can read.
• A track record with a stated basis. Reputation scores whose inputs are published, and witnessing so a receipt is not just our word.
• Audits that outlive the auditor. Proofs anchored on chain, with the methodology and our own audit in the open.

What we refuse

We refuse to sell "agents that decide." That was never defensible and Jev just priced it. We refuse the 200x headline, because against the models a working fleet actually uses it is closer to 1x on cost. We refuse to put a decision model that documents adversarial steering as a known weakness into the one seat whose job is resisting adversarial steering. And we refuse to call a floating point number safe until someone has written down the question it will be asked and the option it gets to take when it does not know.

The second question

If decisions are free, why did you not just adopt Jev? Because we could not get a key, because the weakness sits in our slot, and because at our baseline the saving is a rounding error. We benched the open equivalent instead and the result is above.

What would make this whole argument false? A decision model that stays calibrated under adversarial input, at this price, with open weights. Then the judge layer is a commodity all the way down, and every dollar of margin moves to the record of the decision. That would not weaken the claim. It would be the claim.

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